{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  一、案例简介\n",
    "## 1. 案例背景\n",
    ">   随着时代的进步，航空航天作为我国的战略性发展事业，取得了极大成就。航天事业的发展核心是飞行安全，飞行安全既是飞行人员与乘客的生命安全保障，又是航天科技发展的方向和目标。气象条件对飞行的影响是不同的，也是不可避免的，航天部门应对飞行威胁较大的恶劣天气进行分析，采取相应的对策进行防范，不断提高飞行的安全性与可靠性。\n",
    ">   恶劣天气的类型很多，主要包括大风、云、雷电、暴雨、冰雪，另外，大雾、风切变、冰高空急流等也会对飞行安全产生不同程度的影响。恶劣天气的影响具有不确定性，因而应减少在恶劣天气范围内进行飞行。\n",
    ">   通过天气因素上座数量的分析，预测不同天气情况下航班的上座情况，进而调整飞行计划以应对不同天气带来的影响。\n",
    "\n",
    "## 2. 案例意义\n",
    ">   从分析结果将使航空公司依据天气模式调整飞行时间表。它也可以引导乘客做出新的选择。航空公司可以提前几天预测到未来航班上座情况，然后未雨绸缪地进行调整。航空公司也可以预先发出警告更有效分配自己的资源、地勤人员、飞行人员和其他资产以减少损失\n",
    "## 3. 业务图\n",
    "![业务图](image-001.png)\n",
    "## 4. 案例目标\n",
    ">     现在，大多数航班只能对连续进行重复，一般处理天气延误的航班是在其发生之后。我们的大数据能够让他们预先预测延迟，以更好地与乘客沟通，以及优化资源。通过数据分析，预测假设航班和不同天气情况之间的结果，帮助迅速提前发现因天气发生的上座影响。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、相关技术\n",
    "- ## 数据采集，通过pymysql连接mysql数据库获取数据 \n",
    "- ## 数据预处理，通过pandas、sklearn进行数据清洗，转换等预处理\n",
    "- ## 数据探索，通过matplotlib及pyecharts进行数据可视化，发现数据规律\n",
    "- ## 数据分析建模，通过机器学习算法库sklearn学习已有数据规律建模，预测指定航班在不同天气下的上座率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、案例步骤"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 1. 数据采集: 从数据库casepro中获取数据表t_plane_order\\t_plane_weather,将表格转存在本地csv并展示数据\n",
    "\n",
    "> 数据库信息： host：10.102.52.248，port=3306， user=\"root\", passwd=\"root\"\n",
    "\n",
    "> 航班订单数据，包含航班时刻表，航班号，子订单，飞行日期，头等舱，公务舱，经济舱，其他，头等舱总数，公务舱总数，经济舱总数，其他总数。\n",
    "\n",
    "> 天气数据，包含基线：日期，高温，低温，天气状况，风，空气"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymysql #导入模块\n",
    "import pandas as pd\n",
    "\n",
    "#远程连接数据库\n",
    "db = pymysql.connect(\n",
    "         host='10.102.52.248',\n",
    "         port=3306,\n",
    "         user='root',\n",
    "         passwd='root',\n",
    "         db='casepro',\n",
    "         charset='utf8'\n",
    "         )\n",
    "def link_mysql(db):\n",
    "    #使用cursor()方法获取操作游标 \n",
    "    cursor = db.cursor()\n",
    "    sql = \"\"\"SELECT * FROM `t_plane_order`\"\"\"\n",
    "    sql1 = \"\"\"SELECT * FROM `t_plane_weather`\"\"\"\n",
    "    try:\n",
    "        cursor.execute(sql)  # 执行sql语句\n",
    "        result = cursor.fetchall()\n",
    "        columns = ['航班时刻表','航班号','子订单','日期','头等舱','公务舱','经济舱','其他','头等舱总数','公务舱总数','经济舱总数','其他总数']\n",
    "        frame = pd.DataFrame(list(result),columns=columns)\n",
    "        frame.to_csv(\"航班天气因素上座情况预测分析案例.csv\",encoding='utf-8')\n",
    "        cursor.execute(sql1)  # 执行sql语句\n",
    "        result1 = cursor.fetchall()\n",
    "        columns_weather = ['日期','高温','低温','天气状况','风','空气']\n",
    "        frame_weather = pd.DataFrame(list(result1),columns=columns_weather)\n",
    "        frame_weather.to_csv(\"天气数据.csv\",encoding='utf-8')\n",
    "    except Exception:\n",
    "        db.rollback()  # 发生错误时回滚\n",
    "        print(\"查询失败\")\n",
    "    cursor.close()  # 关闭游标\n",
    "    db.close()  # 关闭数据库连接\n",
    "link_mysql(db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 2、数据预处理（清洗、数值化转换、标准化等）\n",
    "\n",
    "> 空数据判断和处理（注意分析空占比，区分数值型和类别型）\n",
    "\n",
    "> 数据规范性检查（格式、范围等）\n",
    "\n",
    "> 去除不规范数据、无效、无分析价值数据等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#对航班数据进行清理\n",
    "def yucli_plane():\n",
    "    df_plane = pd.read_csv('航班天气因素上座情况预测分析案例.csv',encoding='utf-8')\n",
    "    df_plane = pd.DataFrame(data=df_plane)\n",
    "    df_plane = df_plane.drop(columns='Unnamed: 0')\n",
    "    #判断其空占比\n",
    "    df_plane_percent = df_plane.isna().sum().sort_values(ascending=False) / len(df_plane)\n",
    "    print(\"各字段的空占比为：\\n{}\".format(df_plane_percent))\n",
    "    #删除空数据\n",
    "    df_plane_notNu = df_plane.dropna(axis=0)\n",
    "    #删除重复数据\n",
    "    df_plane_notNu = df_plane_notNu.drop_duplicates()\n",
    "    #将str类型改为float\n",
    "    for i in range(4,12):\n",
    "        df_plane_notNu.iloc[:,i] = df_plane_notNu.iloc[:,i].astype(float)\n",
    "    #将负值替换成零\n",
    "    for i in range(4,12):\n",
    "        df_plane_notNu.iloc[:,i][df_plane_notNu.iloc[:,i] < 0] = 0.0\n",
    "    #删除日期中不规范的数据\n",
    "    df_plane_notNu[df_plane_notNu.iloc[:,3] == \"0.0\"] = np.nan\n",
    "    df_plane_notNu.dropna(axis=0)\n",
    "    return df_plane_notNu\n",
    "\n",
    "#对天气数据进行处理\n",
    "def yucli_weath():\n",
    "    df_weath = pd.read_csv('天气数据.csv',encoding='utf-8')\n",
    "    df_weath = pd.DataFrame(df_weath)\n",
    "    #删除无分析价值数据\n",
    "    df_weath = df_weath.drop('Unnamed: 0',axis=1)\n",
    "    #将日期和空气格式化\n",
    "    df_weath['日期'] = df_weath['日期'].apply(lambda x:str(x)[0:10])\n",
    "    df_weath['空气'] = df_weath['空气'].apply(lambda x:str(x)[:-1])\n",
    "    return df_weath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3249: DtypeWarning: Columns (3,5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "各字段的空占比为：\n",
      "子订单      0.272959\n",
      "航班号      0.013529\n",
      "日期       0.000389\n",
      "其他总数     0.000000\n",
      "经济舱总数    0.000000\n",
      "公务舱总数    0.000000\n",
      "头等舱总数    0.000000\n",
      "其他       0.000000\n",
      "经济舱      0.000000\n",
      "公务舱      0.000000\n",
      "头等舱      0.000000\n",
      "航班时刻表    0.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\lib\\site-packages\\ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
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       "      <th></th>\n",
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       "      <th>日期</th>\n",
       "      <th>航班号</th>\n",
       "      <th>天气状况</th>\n",
       "      <th>风</th>\n",
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       "      <td rowspan=\"5\" valign=\"top\">2016-01-02</td>\n",
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       "      <td>重度污染</td>\n",
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       "      <td>南风1-2级</td>\n",
       "      <td>重度污染</td>\n",
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      "text/plain": [
       "                                         航班时刻表  头等舱  公务舱   经济舱   其他  头等舱总数  \\\n",
       "日期         航班号    天气状况 风       空气                                            \n",
       "2016-01-02 8160   霾~雾  无持续风向微风 严重污染  1615205.0  0.0  0.0   0.0  0.0   16.0   \n",
       "           9581   霾~雾  无持续风向微风 严重污染  2297904.0  0.0  0.0  15.0  0.0   24.0   \n",
       "           BR715  霾~雾  无持续风向微风 严重污染  4525245.0  0.0  0.0   8.0  3.0    0.0   \n",
       "           CA101  霾~雾  无持续风向微风 严重污染   400218.0  0.0  0.0   0.0  0.0   48.0   \n",
       "           CA107  霾~雾  无持续风向微风 严重污染   404502.0  0.0  0.0   2.0  0.0   32.0   \n",
       "...                                        ...  ...  ...   ...  ...    ...   \n",
       "2016-12-31 ZH9157 霾    南风1-2级  重度污染  3144374.0  0.0  0.0  15.0  0.0   24.0   \n",
       "           ZH9159 霾    南风1-2级  重度污染  3949220.0  0.0  0.0   0.0  0.0   24.0   \n",
       "           ZH9163 霾    南风1-2级  重度污染  3068798.0  3.0  0.0  45.0  0.0   24.0   \n",
       "           ZH9165 霾    南风1-2级  重度污染  3072106.0  0.0  0.0  10.0  0.0   24.0   \n",
       "           ZH9168 霾    南风1-2级  重度污染  3083458.0  0.0  0.0  15.0  0.0    0.0   \n",
       "\n",
       "                                     公务舱总数   经济舱总数   其他总数    高温    低温  \n",
       "日期         航班号    天气状况 风       空气                                      \n",
       "2016-01-02 8160   霾~雾  无持续风向微风 严重污染    0.0   324.0    0.0  10.0  -8.0  \n",
       "           9581   霾~雾  无持续风向微风 严重污染    0.0   450.0    0.0  15.0 -12.0  \n",
       "           BR715  霾~雾  无持续风向微风 严重污染  195.0  1190.0  280.0  25.0 -20.0  \n",
       "           CA101  霾~雾  无持续风向微风 严重污染    0.0   483.0    0.0  15.0 -12.0  \n",
       "           CA107  霾~雾  无持续风向微风 严重污染    0.0   322.0    0.0  15.0 -12.0  \n",
       "...                                    ...     ...    ...   ...   ...  \n",
       "2016-12-31 ZH9157 霾    南风1-2级  重度污染    0.0   450.0    0.0  16.0 -20.0  \n",
       "           ZH9159 霾    南风1-2级  重度污染    0.0   450.0    0.0  20.0 -25.0  \n",
       "           ZH9163 霾    南风1-2级  重度污染    0.0   450.0    0.0  16.0 -20.0  \n",
       "           ZH9165 霾    南风1-2级  重度污染    0.0   450.0    0.0  16.0 -20.0  \n",
       "           ZH9168 霾    南风1-2级  重度污染   24.0   480.0    0.0  16.0 -20.0  \n",
       "\n",
       "[200655 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result = pd.merge(left=yucli_plane(), right=yucli_weath(), on=\"日期\", how=\"inner\")\n",
    "df_result.groupby(['日期','航班号','天气状况','风','空气']).sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 3、数据探索，发现数据中的规律"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 4800x3200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']#用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False#用来正常显示负号\n",
    "\n",
    "figure=plt.figure(figsize=(30,20),dpi=160)\n",
    "\n",
    "df_result1=df_result.groupby('风')['头等舱','公务舱','经济舱'].sum()\n",
    "\n",
    "x_ticks=df_result1.index.values\n",
    "x = np.arange(len(x_ticks))\n",
    "plt.xticks(x, x_ticks)\n",
    "\n",
    "y1=df_result1['头等舱'].values\n",
    "y2=df_result1['公务舱'].values\n",
    "y3=df_result1['经济舱'].values\n",
    "\n",
    "plt.xlabel('风')\n",
    "plt.ylabel('上座率')\n",
    "plt.title('风对上座率的影响')\n",
    "\n",
    "\n",
    "width =0.25\n",
    "plt.bar(x,y1,width=width,color=\"blue\")\n",
    "plt.bar(x+width,y2,width=width,color=\"red\")\n",
    "plt.bar(x+2*width,y3,width=width,color=\"orange\")\n",
    "\n",
    "# for a,b in zip(x,y1):\n",
    "#     plt.text(a,b,format(b,','),ha='center',va='bottom',fontsize=14)\n",
    "# for a,b in zip(x,y2):\n",
    "#     plt.text(a+width,b,format(b,','),ha='center',va='bottom',fontsize=14)\n",
    "# for a,b in zip(x,y3):\n",
    "#     plt.text(a+2*width,b,format(b,','),ha='center',va='bottom',fontsize=14)\n",
    "\n",
    "plt.legend(['头等舱','公务舱','经济舱'])#显示图例\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "figure=plt.figure(figsize=(10,10),dpi=100)\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']#用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False#用来正常显示负号\n",
    "\n",
    "\n",
    "df_result2=df_result.groupby('风').sum()\n",
    "# df_result2\n",
    "\n",
    "x_ticks=df_result2.index.values\n",
    "x = np.arange(len(x_ticks))\n",
    "plt.xticks(x, x_ticks)\n",
    "\n",
    "\n",
    "plt.xticks(fontsize=5)\n",
    "plt.yticks(fontsize=8) \n",
    "\n",
    "con1=df_result2['头等舱'].values+df_result2['公务舱'].values+df_result2['经济舱'].values+df_result2['其他'].values\n",
    "con2=df_result2['头等舱总数'].values+df_result2['公务舱总数'].values+df_result2['经济舱总数'].values+df_result2['其他总数'].values\n",
    "\n",
    "\n",
    "\n",
    "width =0.25\n",
    "plt.bar(x,con1,width=width,color=\"orange\")\n",
    "plt.bar(x+width,con2,width=width,color=\"green\")\n",
    "\n",
    "# for a,b in zip(x,con1):\n",
    "#     plt.text(a,b,format(b,','),ha='center',va='bottom',fontsize=5)\n",
    "# for a,b in zip(x,con2):\n",
    "#     plt.text(a+width,b,format(b,','),ha='center',va='bottom',fontsize=5)\n",
    "\n",
    "\n",
    "plt.legend(['其他','其他总数'])#显示图例\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 4、数据分析建模并评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 23.  15. -14.  19.   7.  -4.  -7.  -2.  16.  13.]\n",
      "[ 19.   12.8 -11.2  19.2   7.8  -1.8  -6.4  -0.8  18.4  17. ]\n",
      "0.9325988074667554\n",
      "214    False\n",
      "95     False\n",
      "6      False\n",
      "206    False\n",
      "245    False\n",
      "       ...  \n",
      "98     False\n",
      "141    False\n",
      "190    False\n",
      "189    False\n",
      "64     False\n",
      "Name: 低温, Length: 98, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "# knn回归 KNeighborsRegressor模型\n",
    "import pandas as pd\n",
    "pd.set_option('display.unicode.east_asian_width',True)\n",
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import train_test_split  #分割数据\n",
    "from sklearn.preprocessing import StandardScaler  #用于数据预加工标准化\n",
    "from sklearn.linear_model import LogisticRegression     # 线性模型中的Logistic回归模型\n",
    "# from sklearn.neural_network import MLPClassifier        # 神经网络模型中的多层网络模型\n",
    "from sklearn.svm import LinearSVC                       # SVM模型中的线性SVC模型\n",
    "# from sklearn.linear_model import SGDClassifier          # 线性模型中的随机梯度下降模型\n",
    "data=pd.read_csv('根据天气状况特征预测.csv')\n",
    "# print(data)\n",
    "# X=pd.get_dummies(data['天气状况'])\n",
    "data=data.replace(['中雨','多云','大雪','小雨','晴','阴','阵雨','雨夹雪','雷阵雨','雾','霾'],[0,1,2,3,4,5,6,7,8,9,10])\n",
    "X=data.iloc[:,0:3]\n",
    "y=data.iloc[:,3]\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3)\n",
    "knn=KNeighborsRegressor(n_neighbors=5,n_jobs=-1)\n",
    "knn.fit(X_train,y_train)\n",
    "y_pred=knn.predict(X_test)\n",
    "print(y_test[:10].ravel())\n",
    "print(y_pred[:10])\n",
    "print(knn.score(X_test,y_test))\n",
    "print((y_pred==y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((228, 3), (98, 3))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.unicode.east_asian_width',True)\n",
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import train_test_split  #分割数据\n",
    "from sklearn.preprocessing import StandardScaler  #用于数据预加工标准化\n",
    "from sklearn.linear_model import LogisticRegression     # 线性模型中的Logistic回归模型\n",
    "# from sklearn.neural_network import MLPClassifier        # 神经网络模型中的多层网络模型\n",
    "from sklearn.svm import LinearSVC                       # SVM模型中的线性SVC模型\n",
    "# from sklearn.linear_model import SGDClassifier          # 线性模型中的随机梯度下降模型\n",
    "data=pd.read_csv('根据天气状况特征预测.csv')\n",
    "\n",
    "data=data.replace(['中雨','多云','大雪','小雨','晴','阴','阵雨','雨夹雪','雷阵雨','雾','霾'],[0,1,2,3,4,5,6,7,8,9,10])\n",
    "X=data.iloc[:,0:3]\n",
    "y=data.iloc[:,3]\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3)\n",
    "X_train.shape,X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "逻辑回归:\n",
      "Training set score: 0.118\n",
      "Testing set score: 0.031\n",
      "普通线性回归:\n",
      "Training set score: 0.940\n",
      "Testing set score: 0.906\n",
      "岭回归:\n",
      "Training set score: 0.940\n",
      "Testing set score: 0.906\n",
      "lasso:\n",
      "Training set score: 0.940\n",
      "Testing set score: 0.906\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression,LinearRegression,Ridge,Lasso\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']#用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False#用来正常显示负号\n",
    "\n",
    "\n",
    "#linear_model模块调用逻辑回归模型\n",
    "lr = LogisticRegression(C=100)\n",
    "lr.fit(X_train, y_train)\n",
    "y_pre_lr = lr.predict(X_test)#使用测试数据，获取预测结果\n",
    "print(\"逻辑回归:\")\n",
    "print(\"Training set score: {:.3f}\".format(lr.score(X_train, y_train)))\n",
    "print(\"Testing set score: {:.3f}\".format(lr.score(X_test, y_test)))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#普通线性回归模型\n",
    "line=LinearRegression()\n",
    "line.fit(X_train,y_train)\n",
    "y_pre_line = line.predict(X_test)#使用测试数据，获取预测结果\n",
    "print(\"普通线性回归:\")\n",
    "print(\"Training set score: {:.3f}\".format(line.score(X_train, y_train)))\n",
    "print(\"Testing set score: {:.3f}\".format(line.score(X_test, y_test)))\n",
    "\n",
    "\n",
    "#岭回归模型\n",
    "ridge = Ridge(alpha=0.5)\n",
    "ridge.fit(X_train,y_train)\n",
    "y_pre_ridge = ridge.predict(X_test)#使用测试数据，获取预测结果\n",
    "print(\"岭回归:\")\n",
    "print(\"Training set score: {:.3f}\".format(ridge.score(X_train, y_train)))\n",
    "print(\"Testing set score: {:.3f}\".format(ridge.score(X_test, y_test)))\n",
    "\n",
    "\n",
    "#lasso回归模型\n",
    "lasso = Lasso(alpha=0.003)\n",
    "lasso.fit(X_train,y_train)\n",
    "y_pre_lasso = line.predict(X_test)#使用测试数据，获取预测结果\n",
    "print(\"lasso:\")\n",
    "print(\"Training set score: {:.3f}\".format(lasso.score(X_train, y_train)))\n",
    "print(\"Testing set score: {:.3f}\".format(lasso.score(X_test, y_test)))\n",
    "\n",
    "\n",
    "#普通线性回归\n",
    "plt.plot(np.arange(y_pre_line.size),y_pre_line)\n",
    "plt.plot(np.arange(y_test.size),y_test)\n",
    "\n",
    "plt.title('普通线性回归')\n",
    "plt.show()\n",
    "\n",
    "#岭回归模型\n",
    "plt.plot(np.arange(y_pre_ridge.size),y_pre_ridge,color='orange')\n",
    "plt.plot(np.arange(y_test.size),y_test,color='green')\n",
    "\n",
    "plt.title('岭回归模型')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 5、整理工程并部署：输入航班号、根据未来一周的天气（网上爬取的天气预报），预测并输出其上座率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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